Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
Real-time top-n recommendation in social streams
Proceedings of the sixth ACM conference on Recommender systems
Proceedings of the 21st ACM international conference on Information and knowledge management
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The Web of people is highly dynamic and the life experiences between our on-line and "real-world" interactions are increasingly interconnected. For example, users engaged in the Social Web more and more rely upon continuous social streams for real-time access to information and fresh knowledge about current affairs. However, given the deluge of data items, it is a challenge for individuals to find relevant and appropriately ranked information at the right time. Having Twitter as test bed, we tackle this information overload problem by following an online collaborative approach. That is, we go beyond the general perspective of information finding in Twitter, that asks: "What is happening right now?", towards an individual user perspective, and ask: "What is interesting to me right now within the social media stream?". In this paper, we review our recently proposed online collaborative filtering algorithms and outline potential research directions.